The objective of this proposal is to develop a predictive model to identify individuals who are infected with SARS-CoV-2 and at risk of developing severe COVID-19. Louisiana has the 5th highest death rate per capita in the United States as of May 4th, 2020. Severe disease is seen in older individuals and those with underlying conditions. The New Orleans population is particularly susceptible to severe COVID-19 as hypertension, diabetes and obesity are rampant. After infection, acute lung injury caused by the virus must be repaired to regain lung function and avoid acute respiratory distress syndrome and pulmonary fibrosis. Mounting evidence suggests that patients with severe COVID-19 have cytokine storm syndrome, which may exacerbate multiorgan injury and risk of fibrotic complications. Lack of effective ways to identify and attenuate severe COVID-19 progression persist due to limited understanding of the biological pathways responsible for cytokine storm syndrome and increased risk in older patients. Therefore, there is a need to determine the critical cytokine profiles responsible for severe COVID-19 progression to develop effective treatments. Further, it is essential to find a way to stage disease trajectory(ies) to identify therapeutic targets with precision to attenuate disease progression and uncover preventive strategies. Towards this end, we seek to leverage a mathematical model of SARS-CoV-2-induced lung damage to predict severity of acute respiratory distress syndrome and pulmonary fibrosis by considering key cytokine-cell interactions. We hypothesize that the model will accurately predict quantitative changes in suites of key cytokines and matrix accumulation with varying COVID-19 progression within 10% accuracy. To accomplish this, we have assembled an investigative team at Tulane University with key expertise in virology, clinical infectious disease research, bioinformatics, and predictive mathematical models of tissue remodeling. In Aim 1 of the proposal, we will identify the critical cytokine markers linked to viral-induced lung damage and pulmonary fibrosis. This will be accomplished by leveraging machine learning to determine the biomarkers and molecular pathways characterizing progression of severe COVID-19 to focus model formulation. In Aim 2, we will predict the severity of COVID-19 in older patients. Model predictions will be compared to blood markers of COVID-19 disease in cohorts of older patients at different stages of disease progression. The model will be refined and informed by cytokine data to discern causal biological pathways and disease processes that can be tested and targeted. Our expected outcome is to have determined the critical cytokine interactions responsible for lung tissue damage and dictating pathways for varying disease trajectories in older patients. These results are expected to have an important impact as the proposed predictive model will open new avenues of research to rationally design pharmaceutical interventions for severe COVID-19 patients. Specifically, the study will provide a paradigm-shifting open-source tool to delineate target therapeutics, estimate their efficacy, and move towards development of patient-specific treatment plans for older individuals.